Application of digital twins for simulation based tailoring of laser induced graphene

In the era of man–machine interfaces, digital twins stand as a key technology, offering virtual representations of real-world objects, processes, and systems through computational models. They enable novel ways of interacting with, comprehending, and manipulating real-world entities within a virtual realm. The real implementation of graphene-based sensors and electronic devices remains challenging due to the integration complexities of high-quality graphene materials with existing manufacturing processes. To address this, scalable techniques for the in-situ fabrication of graphene-like materials are essential. One promising method involves using a CO2 laser to convert polyimide into graphene. Optimizing this graphitization process is hindered by complex parameter interactions and nonlinear terms. This article explores how these digital replicas can enhance the fabrication of laser-induced graphene (LIG) through laser simulation and machine learning methods to enable rapid single-step LIG patterning. This approach aims to create a universal simulation for all CO2 lasers, calculating optical energy flux and utilizing machine learning to control and predict LIG conductivity (ability to conduct current), morphology, and electrical resistance. The proposed procedure, integrating digital twins in the LIG production process, will avoid or reduce the preliminary tests required to determine the proper laser parameters to reach the desired LIG characteristics. Accordingly, this approach will reduce the time and costs associated with these tests and thus increase the efficiency and optimize the procedure.


Simulation methodology:
In our efforts to provide a tool for tailoring the fabrication of laser-induced graphene (LIG), we have developed an algorithm rooted in our Simulink simulation models that predicts essential LIG characteristics.This algorithm is a critical advancement for the field, as it systematically evaluates and predicts LIG's conductivity, sheet resistance, and morphology based on a range of configurable laser parameters.To convey the algorithm's workflow and its practical applications, we have introduced a flowchart in Figure 1 of the article.This flowchart begins with the acquisition of foundational laser parameters-fluency, power density, and peak power-from both configurable and fixed settings within the simulation framework.These parameters are crucial as they underpin the LIG's subsequent characteristics.Following this, the algorithm assesses whether the resulting LIG sample is conductive.For conductive samples, it goes a step further to calculate sheet resistance, offering insights critical to the material's electronic utility.It does not stop there; the algorithm also evaluates the morphology of the LIG, providing an integrated view of its structural attributes.The algorithm has been designed to serve not just as an analytical tool but also as a decision-making aid for tailoring LIG properties to specific application needs.The adaptability and depth of our model are encapsulated within the accompanying MATLAB script, which we have made available on GitHub to the research community.This script includes annotations explaining each function and command, ensuring that users can both understand and modify the code as needed for their unique parameters and targets.This MATLAB script, named simulationLIGFabricatedScript.m on GitHub, is designed to simulate the fabrication process of laser-induced graphene (LIG) and predict its key characteristics.Initially, the script sets both configurable and fixed laser parameters to accurately represent the experimental setup.These include laser power, speed, frequency, and physical attributes like beam size and raising time.It then performs a series of computations to determine the interaction time of the laser with the substrate, the energy delivered per pulse, and the total energy over the drawn line.The heart of the simulation lies within a Simulink model, named laserSim.slxon GitHub, which models the complex dynamics between the laser and the substrate.Post-simulation, the script calculates the energy distribution and fluency, providing insights into the localized effects of the laser on the material properties.Using machine learning models, the script predicts the conductivity and morphology of the LIG samples, such as woolly fibers, cellular networks, or porous formations, and estimates the sheet resistance.These predictions allow for an assessment of the material's electrical performance and structural qualities.The script culminates by visually representing the laser's path and energy deposition, offering an intuitive understanding of the laser writing process.

Figure S3 .
Figure S3.The charts show the relationship between the classification morphology of the LIG and (a) laser power duty cycle, (b) laser speed, and (c) laser fluency.

Figure S4 .
Figure S4.The charts show (a) scatter plots and correlation plots of (b) training and (c) testing procedures for the data set used all classified conductive samples without any resistance cut-off.

Figure S5 .
Figure S5.The charts show the relationship between the LIG sheet resistance and (a) laser power duty cycle, (b) laser speed, and (c) laser fluency.

Figure S6 .
Figure S6.The charts show the relationship between the LIG sheet resistance and their morphology

Table S2 .
The table shows the results of the training and validation of the morphology classification model, for different distributions of the dataset.

Table S3 .
The table shows the results of the training and validation of the estimation LIG sheet resistance model, for different distributions of the dataset.